External validation of SpineNetv2 deep learning system for automated lumbar spine MRI analysis: A multi-pathology diagnostic agreement study

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BACKGROUND: Magnetic resonance imaging (MRI) is the reference standard for evaluating degenerative lumbar spine disorders, but interpretation is time-consuming and subject to inter-observer variability. SpineNetv2, a publicly available deep learning system, enables automated analysis of multiple spinal pathologies. This study conducted an independent external validation of SpineNetv2 against expert reference assessments. METHODS: A total of 491 patients (2,455 lumbar discs, L1/2-L5/S1) were retrospectively included. Disc-level reference assessments were provided by an expert orthopedic surgeon, with a junior orthopedic surgeon serving as comparator. Six pathologies were assessed: disc degeneration (Pfirrmann grading), central canal stenosis (CCS), spondylolisthesis, herniation, and bilateral foraminal stenosis (FS). Performance metrics included sensitivity, specificity, positive predictive value, negative predictive value, F1-score, Matthews correlation coefficient, exact agreement, weighted kappa, and mean absolute error. McNemar's test and bootstrap resampling (1,000 iterations) were used for statistical analysis. RESULTS: Overall agreement ranged from 83.5% to 97.5% (mean 92.8%). SpineNetv2 significantly outperformed the junior orthopedic surgeon in CCS, spondylolisthesis, and bilateral FS (all p ≤ 0.001), with comparable performance in herniation (p = 0.293). Pfirrmann grading showed lower MAE for SpineNetv2 compared with the junior surgeon (0.213 vs. 0.254, p = 0.001), though agreement declined in older patients and upper lumbar discs. Error analysis revealed a specificity-oriented profile, with false negatives exceeding false positives. CONCLUSIONS: SpineNetv2 demonstrated high agreement across five binary lumbar pathologies, while Pfirrmann grading remained the main limitation, particularly in elderly upper lumbar discs. Its specificity-oriented profile supports use as a confirmatory second reader, but reliance on negative findings is not recommended. Broader reliability will require multicenter, multi-reader validation and sensitivity-oriented calibration.

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